In numerical weather prediction (NWP), the uncertainty about the future state of the atmosphere is described by a set of forecasts (called an ensemble). All ensembles have deficiencies that can be corrected via statistical post‐processing methods. Several ensembles, based on different NWP models, exist and may be corrected using different statistical methods. These raw or post‐processed ensembles can thus be combined. The theory of prediction with expert advice allows us to build combination algorithms with theoretical guarantees on the forecast performance. We adapt this theory to the case of probabilistic forecasts issued as stepwise cumulative distribution functions, computed from raw and post‐processed ensembles. The theory is applied to combine wind speed ensemble forecasts. The second goal of this study is to explore the use of two forecast performance criteria: the continuous ranked probability score (CRPS) and the Jolliffe–Primo test. The usual way to build skilful probabilistic forecasts is to minimize the CRPS. Minimizing the CRPS may not produce reliable forecasts according to the Jolliffe–Primo test. The Jolliffe–Primo test generally selects reliable forecasts, but could lead to issuing suboptimal forecasts in terms of CRPS. We propose to use both criteria to achieve reliable and skilful probabilistic forecasts.

中文翻译：

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概率预测的顺序汇总—在风速集合预报中的应用

在数值天气预报（NWP）中，关于大气未来状态的不确定性由一组预报（称为集合）描述。所有合奏都有缺陷，可以通过统计后处理方法进行纠正。存在基于不同NWP模型的多个合奏，并且可以使用不同的统计方法进行校正。因此，可以将这些原始或后处理的合奏进行组合。通过专家建议的预测理论，我们可以构建具有预测性能理论保证的组合算法。我们将此理论调整为概率预测的情况，概率预测是作为逐步累积分布函数发布的，该函数是根据原始和后处理的合奏计算得出的。该理论适用于组合风速集合预报。这项研究的第二个目标是探索两个预测绩效标准的使用：连续排名概率评分（CRPS）和Jolliffe–Primo检验。建立熟练的概率预测的通常方法是最小化CRPS。根据Jolliffe–Primo测试，将CRPS最小化可能无法产生可靠的预测。Jolliffe–Primo检验通常选择可靠的预测，但可能导致发布CRPS方面的次优预测。我们建议使用这两个标准来获得可靠和熟练的概率预测。Jolliffe–Primo检验通常选择可靠的预测，但可能导致发布CRPS方面的次优预测。我们建议使用这两个标准来获得可靠和熟练的概率预测。Jolliffe–Primo检验通常选择可靠的预测，但可能导致发布CRPS方面的次优预测。我们建议使用这两个标准来获得可靠和熟练的概率预测。